#include <cmath>
#include <iostream>

#include "common.hpp"
#include "functions.hpp"
#include <opencv2/highgui/highgui.hpp>


#ifdef USE_NVTX
#include "nvToolsExt.h"

const uint32_t colors[] = { 0xff000000, 0xff0000ff,
							0xff00ff00, 0xff00ffff,
							0xffff0000, 0xffff00ff,
							0xffffff00, 0xffffffff,
							0xffcccccc };
const int num_colors = sizeof(colors)/sizeof(uint32_t);

// CUSTOMIZABLE CATEGORY - MEMORY - COMPUATION
#define PUSH_RANGE(name,cid,cat) { \
    int color_id = cid; \
    color_id = color_id%num_colors;\
    nvtxEventAttributes_t eventAttrib = {0}; \
    eventAttrib.version = NVTX_VERSION; \
    eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; \
    eventAttrib.category = cat; \
    eventAttrib.colorType = NVTX_COLOR_ARGB; \
    eventAttrib.color = colors[color_id]; \
    eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; \
    eventAttrib.message.ascii = name; \
    nvtxRangePushEx(&eventAttrib); \
}
#define POP_RANGE nvtxRangePop();
#else
#define PUSH_RANGE(name,cid,category)
#define POP_RANGE
#endif
#ifndef MEASURE_ALL
#define MEASURE_ALL(all, m) \
    MeasureReturn CONCAT(tmpret_, __LINE__) = m;\
    all.add(CONCAT(tmpret_, __LINE__)); 
#endif

const int MEMORY_CATEGORY = 1;
const int COMPUTATION_CATEGORY = 2;

Collector c;

static int bit_pattern_31_[256*4] =
{
    8,-3, 9,5/*mean (0), correlation (0)*/,
    4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
    -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
    7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
    2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
    1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
    -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
    -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
    -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
    10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
    -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
    -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
    7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
    -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
    -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
    -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
    12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
    -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
    -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
    11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
    4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
    5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
    3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
    -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
    -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
    -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
    -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
    -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
    -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
    5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
    5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
    1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
    9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
    4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
    2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
    -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
    -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
    4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
    0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
    -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
    -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
    -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
    8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
    0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
    7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
    -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
    10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
    -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
    10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
    -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
    -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
    3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
    5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
    -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
    3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
    2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
    -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
    -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
    -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
    -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
    6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
    -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
    -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
    -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
    3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
    -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
    -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
    2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
    -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
    -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
    5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
    -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
    -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
    -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
    10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
    7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
    -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
    -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
    7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
    -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
    -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
    -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
    7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
    -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
    1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
    2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
    -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
    -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
    7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
    1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
    9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
    -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
    -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
    7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
    12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
    6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
    5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
    2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
    3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
    2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
    9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
    -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
    -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
    1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
    6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
    2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
    6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
    3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
    7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
    -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
    -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
    -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
    -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
    8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
    4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
    -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
    4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
    -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
    -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
    7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
    -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
    -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
    8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
    -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
    1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
    7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
    -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
    11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
    -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
    3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
    5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
    0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
    -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
    0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
    -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
    5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
    3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
    -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
    -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
    -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
    6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
    -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
    -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
    1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
    4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
    -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
    2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
    -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
    4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
    -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
    -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
    7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
    4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
    -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
    7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
    7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
    -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
    -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
    -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
    2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
    10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
    -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
    8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
    2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
    -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
    -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
    -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
    5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
    -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
    -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
    -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
    -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
    -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
    2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
    -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
    -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
    -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
    -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
    6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
    -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
    11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
    7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
    -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
    -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
    -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
    -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
    -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
    -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
    -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
    -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
    1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
    1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
    9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
    5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
    -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
    -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
    -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
    -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
    8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
    2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
    7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
    -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
    -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
    4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
    3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
    -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
    5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
    4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
    -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
    0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
    -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
    3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
    -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
    8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
    -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
    2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
    10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
    6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
    -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
    -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
    -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
    -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
    -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
    4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
    2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
    6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
    3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
    11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
    -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
    4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
    2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
    -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
    -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
    -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
    6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
    0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
    -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
    -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
    -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
    5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
    2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
    -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
    9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
    11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
    3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
    -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
    3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
    -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
    5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
    8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
    7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
    -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
    7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
    9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
    7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
    -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
};


ORBFunctions::ORBFunctions(int _width, int _height, float _scaleFactor, int _nlevels,
                 int _iniThFAST, int _minThFAST, int _nfeatures, std::string suffix) : width(_width), height(_height), threads(5), /*ss1(0), ss2(0),*/ nfeatures(_nfeatures),
                  scaleFactor(_scaleFactor), nlevels(_nlevels),
                 iniThFAST(_iniThFAST), minThFAST(_minThFAST) {
	std::cout << "Init: " << width << "x" << height << ", " << _nfeatures << " features!" << std::endl;
	mvScaleFactor[0]=1.0f;
    mvLevelSigma2[0]=1.0f;
    images[0].init(width, height);
    imagesGauss[0].init(width, height);
    d_scores[0].init(images[0].cols, images[0].rows);
    for(int i=1; i<nlevels; i++)
    {
        mvScaleFactor[i]=mvScaleFactor[i-1]*scaleFactor;
        mvLevelSigma2[i]=mvScaleFactor[i]*mvScaleFactor[i];
        mvInvScaleFactor[i]=1.0f/mvScaleFactor[i];
        mvInvLevelSigma2[i]=1.0f/mvLevelSigma2[i];
        
        images[i].init(std::lround(width*mvInvScaleFactor[i]), std::lround(height*mvInvScaleFactor[i]));
        imagesGauss[i].init(std::lround(width*mvInvScaleFactor[i]), std::lround(height*mvInvScaleFactor[i]));
        d_scores[i].init(images[i].cols, images[i].rows);
    }
    
    for(int i=0; i<nlevels; i++)
    {
		images[i].name = "IMAGE " + std::to_string(i+1);
		imagesGauss[i].name = "IMAGE GAUSSIAN " + std::to_string(i+1);
		kp_fast[i].name = "KP " + std::to_string(i+1);
	}

    float factor = 1.0f / scaleFactor;
    float nDesiredFeaturesPerScale = /*N_FEATURES*/ nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));

    int sumFeatures = 0;
    for( int level = 0; level < nlevels-1; level++ )
    {
        mnFeaturesPerLevel[level] = std::lround(nDesiredFeaturesPerScale);
        sumFeatures += mnFeaturesPerLevel[level];
        nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels-1] = std::max(/*N_FEATURES*/ nfeatures - sumFeatures, 0);

    const int npoints = 512;
    const cv::Point* pattern0 = (const cv::Point*)bit_pattern_31_;
    std::copy(pattern0, pattern0 + npoints, pattern);

    //This is for orientation
    // pre-compute the end of a row in a circular patch
    int v, v0, vmax = std::floor(HALF_PATCH_SIZE_C * sqrt(2.f) / 2 + 1);
    int vmin = std::ceil(HALF_PATCH_SIZE_C * sqrt(2.f) / 2);
    const double hp2 = HALF_PATCH_SIZE_C*HALF_PATCH_SIZE_C;
    for (v = 0; v <= vmax; ++v)
        umax[v] = std::lround(sqrt(hp2 - v * v));

    // Make sure we are symmetric
    for (v = HALF_PATCH_SIZE_C, v0 = 0; v >= vmin; --v)
    {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }
    
    cudaStream_t stream2;
    CUDA_SAFE_CALL(cudaStreamCreate(&stream2));
    for( int level = 0; level < nlevels; level++ )
    {
		images[level].streamMemory = stream2;
		images[level].level = level+1;
		images[level].name = "IMAGE" + suffix;
		
		imagesGauss[level].streamMemory = stream2;
		imagesGauss[level].level = level+1;
		imagesGauss[level].name = "IMAGE_GAUSS" + suffix;
		
		kp_fast[level].streamMemory = stream2;
		kp_fast[level].level = level+1;
		kp_fast[level].name = "KP_FAST" + suffix;
		
		kp_grid[level].streamMemory = stream2;
		kp_grid[level].level = level+1;
		kp_grid[level].name = "KP_GRID" + suffix;
		
		kp_quadtree[level].streamMemory = stream2;
		kp_quadtree[level].level = level+1;
		kp_quadtree[level].name = "KP_QUADTREE" + suffix;
		kp_quadtree[level].setMaxCount(mnFeaturesPerLevel[level]);
		
		kp_angle[level].streamMemory = stream2;
		kp_angle[level].level = level+1;
		kp_angle[level].name = "KP_ANGLE" + suffix;
		kp_angle[level].setMaxCount(mnFeaturesPerLevel[level]);
		
		kp_final[level].streamMemory = stream2;
		kp_final[level].level = level+1;
		kp_final[level].name = "KP_FINAL" + suffix;	
	
		descriptor[level].init(32, 70000);
		descriptor[level].streamMemory = stream2;
		descriptor[level].level = level+1;
		descriptor[level].name = "DESCRIPTORS" + suffix;
	}
	
	CUDA_SAFE_CALL(cudaStreamCreate(&stream));
    
    /*
    //for scale 2
    images[0].increaseOut();
    //for fast 1
    images[0].increaseOut();
    //for gaussian 1
    images[0].increaseOut();
    */
    
    deepLearning[0] = new DeepLearning(&images[0], 10000); // default: 10 millisecond
    deepLearning[0]->name = "DEEP LEARNING" + suffix;
    deepLearning[0]->stream = stream;
    
    for( int level = 0; level < nlevels; level++ )
    {
		fast_k[level] = new FAST_kernel(&images[level], &d_scores[level], &kp_fast[level], 7, true, CPU);
		fast_k[level]->level = level+1;
		fast_k[level]->stream = stream;
		fast_k[level]->name = "FAST" + suffix;
		
		gaussian[level] = new Gaussian(&images[level], &imagesGauss[level], CPU);
		gaussian[level]->stream = stream;
		gaussian[level]->name = "GAUSSIAN" + suffix;
		gaussian[level]->level = level+1;
		
		
		int min_x = EDGE_THRESHOLD; //-3;
		int min_y = EDGE_THRESHOLD; //-3;
		int max_x = images[level].cols-EDGE_THRESHOLD; //+3;
		int max_y = images[level].rows-EDGE_THRESHOLD; //+3;
		
		std::cout << max_x  << " -- " << max_y << std::endl;
		
		grid[level] = new ComputeGrid(&kp_fast[level], &kp_grid[level], 
			min_x, min_y, max_x, max_y,
			20, CPU);
		grid[level]->name = "GRID" + suffix;
		grid[level]->level = level+1;
		
		
		quadtree[level] = new ComputeQuadtree(&kp_grid[level], &kp_quadtree[level],  images[level].cols,
			min_x, min_y, max_x, max_y,
			mnFeaturesPerLevel[level], CPU);//TODO: reserve variable feature per level
		quadtree[level]->name = "QUADTREE" + suffix;
		quadtree[level]->level = level+1;
		
		
		angle[level] = new ComputeAngle(&images[level], &kp_quadtree[level], &kp_angle[level], 
			std::vector<int>(umax, umax+HALF_PATCH_SIZE_C+1), CPU);
		angle[level]->name = "ANGLE" + suffix;
		angle[level]->level = level+1;
		
		orb[level] = new ORB(&imagesGauss[level], &kp_angle[level], &descriptor[level], CPU);
		orb[level]->name = "ORB" + suffix;
		orb[level]->level = level+1;
		
		scaleVector[level] = new ScaleCustomVector(&kp_angle[level], &kp_final[level], mvScaleFactor[level], PATCH_SIZE_C*mvScaleFactor[level], CPU);
		scaleVector[level]->name = "SCALE_VECTOR" + suffix;
		scaleVector[level]->level = level+1;
	}
    for( int level = 0; level < nlevels-1; level++ )
    {
		scales[level] = new Scale(&images[level], &images[level+1], CPU);
		scales[level]->stream = stream;
		scales[level]->name = "SCALE" + suffix;
		scales[level]->level = level+2;
	}
	
}

MeasureReturn ORBFunctions::prepareFirstImage(cv::Mat image) {
	MeasureReturn m(currentFrameNumber);
    //cv::imshow("test", image);
    //cv::waitKey(0);
    uchar* imageData = image.data;
    MEASURE(m, "CPU", "computation", "SCALE", "scale", 0,
    std::copy(imageData, imageData+image.rows*image.cols, (uchar*)images[0].img.data);
    )

    images[0].setWhere(CPU);
    //images[0].finishProcessing();
    return m;
}

void ORBFunctions::thread_run(int idx) 
{ 
	mutexes[idx].unlock();
	
	while(continueToExecute) {
		//s2->wait("S2 WAIT THD " + std::to_string(idx) + " - ");//barrier to enable to stop the execution
		mutexes_main[idx].lock();
		
		//fprintf(stderr, "Idx %d started \n", idx);
		
		if(!continueToExecute) {
			break;
		}
		
		MeasureReturn m(fn);
		elaborateQueue(&QUEUES[idx], idx+1, m);
		MEASURE_ALL(all, m);
		
		//s1->notify("S1 RELEASE THD " + std::to_string(idx) + " - ");//tell that I elaborated my queue
		
		// another barrier
		mutexes[idx].unlock();
	}
	
	std::cout << "Thread " << idx << " exiting" << std::endl;
}


void ORBFunctions::init(bool global_optimum, bool gpu) {
	ORBFunctions& o = *this;
 
	//assign to pe
	for(int i = 0; i < nlevels - 1; i++) {
		o.scales[i]->where = CPU;
	}
	o.scales[0]->where = CPU;
	
	for(int i = 0; i < nlevels; i++) {
		o.fast_k[i]->where = CPU;
	}
	
	for(int i = 0; i < nlevels; i++) {
		o.gaussian[i]->where = CPU;
	}
	
	
	for(int i = 0; i < nlevels; i++) {
		o.images[i].setToBeTransferred(true);
		o.images[i].setWhere(CPU);
		o.imagesGauss[i].setWhere(CPU);
		o.imagesGauss[i].setToBeTransferred(true);
		
		o.kp_grid[i].setWhere(CPU);
	}
	o.images[0].setWhere(CPU);
	
		
	int curIdx = 0;
	
	if(global_optimum) { // COMPOSITION OF GLOBAL OPTIMUM
		std::cout << "Global optimum selection" << std::endl;
		#include "schedule.global.i.cc"
	} else {
		// LOCAL optimum
		std::cout << "Local optimum selection" << std::endl;
		#include "schedule.local.i.cc"
	
	}
	
	if(gpu) {
		for( auto kk : QUEUES[0] ) {
			kk.first->setWhere(GPU);
		}
			
		for( int i = 0; i < 6; ++i ) {
			for( auto kk : QUEUES[i] ) {
				kk.first->shouldTransferOutput(kk.second);
			}
		}
	}
	
	curIdx++;
	
	/*s1 = &ss1;
	s2 = &ss2;*/
	
	// sync mechanism
	
	int fn = o.currentFrameNumber;
	MeasureReturnAll *measures = &all;
	
	continueToExecute = true;
	//la coda 0 (GPU) elaborata dal thread corrente
	for (int i = 1; i < num_threads; ++i) {
		std::cout << "Creating thread " << i << std::endl;
		mutexes[i].lock();
		mutexes_main[i].lock();
		
		//auto q = &QUEUES[i];
		threads[i-1] = std::thread(&ORBFunctions::thread_run, this, i );
	}
	
	//wait to get tid
	for (unsigned i = 1; i < num_threads; ++i) {
		mutexes[i].lock();//s1->wait();
	}
#ifdef USE_NVTX	
	nvtxNameOsThread(pthread_self(), "GPU (Denver 1)");
	
	nvtxNameOsThread(threads[0].native_handle(), "ARM 1");
	nvtxNameOsThread(threads[1].native_handle(), "ARM 2");
	nvtxNameOsThread(threads[2].native_handle(), "ARM 3");
	nvtxNameOsThread(threads[3].native_handle(), "ARM 4");
	nvtxNameOsThread(threads[4].native_handle(), "Denver 2");
	
	nvtxNameCategory(MEMORY_CATEGORY, "Memory transfer");
	nvtxNameCategory(COMPUTATION_CATEGORY, "Computational load");
#endif
	
	int CPUS[6];
	CPUS[0] = global_cpu;//Denver 1
	CPUS[1] = 0;//ARM 1
	CPUS[2] = 3;//ARM 2
	CPUS[3] = 4;//ARM 3
	CPUS[4] = 5;//ARM 4
	CPUS[5] = 2;//Denver 2
	for (unsigned i = 0; i < 1; ++i) {
		// Create a cpu_set_t object representing a set of CPUs. Clear it and mark
		// only CPU i as set.
		cpu_set_t cpuset;
		CPU_ZERO(&cpuset);
		CPU_SET(CPUS[i], &cpuset);
		int rc = pthread_setaffinity_np(pthread_self(),
										sizeof(cpu_set_t), &cpuset);
		if (rc != 0) {
		  std::cerr << "Error calling pthread_setaffinity_np: " << rc << "\n";
		}
	}
	// tutti thread pronti
	for (unsigned i = 1; i < num_threads; ++i) {
		// Create a cpu_set_t object representing a set of CPUs. Clear it and mark
		// only CPU i as set.
		cpu_set_t cpuset;
		CPU_ZERO(&cpuset);
		int cpu = CPUS[i];
		if(cpu == global_cpu) cpu = 1;
		CPU_SET(cpu, &cpuset);
		int rc = pthread_setaffinity_np(threads[i-1].native_handle(),
										sizeof(cpu_set_t), &cpuset);
		if (rc != 0) {
		  std::cerr << "Error calling pthread_setaffinity_np: " << rc << "\n";
		}
	}
}


void ORBFunctions::elaborateQueue(std::vector<pk>* Q, int idx_thread, MeasureReturn& m, bool print) {
	int idx = 2;
	std::vector<pk>& queue = QUEUES[idx_thread-1];//*Q
	
	//fprintf(stderr, "STA Thread idx %d started with size %d #%d \n", idx_thread, queue.size(), nfeatures);

	for(int i = 0; i < queue.size(); i++) {
		auto kk = queue[i];
		auto k = kk.first;
		auto shouldTransfer = kk.second;
		int cid = k->cid;
	
	/*
		idx+=3;
		std::string ii_3 = std::to_string(100*idx_thread + idx-3);
		std::string ii_2 = std::to_string(100*idx_thread + idx-2);
		std::string ii_1 = std::to_string(100*idx_thread + idx-1);
		ii_1 = std::string(3-ii_1.length(), '0') + ii_1;
		ii_2 = std::string(3-ii_2.length(), '0') + ii_2;
		ii_3 = std::string(3-ii_3.length(), '0') + ii_3;
		*/
		//std::string name = /*ii_2 + " - " + */k->name + " " + std::to_string(k->level) + " @ " + ( (k->where == GPU) ? "GPU" : "CPU");
		//std::string transfer_name = "TRANSFER " + k->name + " " + std::to_string(k->level) + " @ " + ( (k->where == GPU) ? "GPU" : "CPU");
		//std::string transfer_name_in = /*ii_3 + " - " + */"IN " + transfer_name;
		//std::string transfer_name_out = /*ii_1 + " - " + */"OUT " + transfer_name;*/
		
		//PUSH_RANGE(transfer_name_in.c_str(), cid, MEMORY_CATEGORY)
		k->transfer_input();
		//POP_RANGE
		
		
		//std::string kernel_name = /*ii_2 + " - " + */"KERNEL " + k->name + " " + std::to_string(k->level) + " @ " + ( (k->where == GPU) ? "GPU" : "CPU");
		//std::string kernel_name_in = "IN " + kernel_name;
		//std::string kernel_name_out = "OUT " + kernel_name;
		
		//fprintf(stderr, "MID Thread idx %d started with size %d, idx %d\n", idx_thread, queue.size(), i);
		
		//PUSH_RANGE(name.c_str(), cid, COMPUTATION_CATEGORY)
		//MEASURE(m, k->where == CPU ? "CPU" : "GPU", "execution", k->name, "start", k->level,
		k->execute();
		k->wait(); //wait kernel completition
		//)
		//POP_RANGE
		
		if(shouldTransfer) {
			//PUSH_RANGE(transfer_name_out.c_str(), cid, MEMORY_CATEGORY)
			k->transfer_output();
			k->wait_output();
			//POP_RANGE
		} else {
			k->transfer_output();
		}
	}
	
	//fprintf(stderr, "END Thread idx %d started with size %d \n", idx_thread, queue.size());
}

void ORBFunctions::execute(cv::Mat im, MeasureReturnAll& all) {
const int N_T = num_threads;
  ORBFunctions& o = *this; 
  //fprintf(stderr, "START COMPUTATION \n");
  PUSH_RANGE(("Frame nr " + std::to_string(fn+1)).c_str(), 0, 0)
	PUSH_RANGE("Computing image", 0, 0)
	MeasureReturn m(o.currentFrameNumber);
	//std::cout << "Preparing " << std::to_string(fn) << std::endl << std::flush;
	//std::cerr << "--" << std::endl;
	//Semaphore *s1 = &ss1;
	//Semaphore *s2 = &ss2;
	//MEASURE(m, "CPU", "preparation", "THREADS", "tread", 0,
	//		);
	
	std::swap(c.v, c.v_old);
	c.v.clear();
	
	
	//flag all images as dirty
	for(int i = 0; i < nlevels; ++i) {
		o.images[i].setDirt();
		o.imagesGauss[i].setDirt();
		
		o.kp_fast[i].setDirt();
		o.kp_grid[i].setDirt();
		o.kp_quadtree[i].setDirt();
		o.kp_quadtree[i].setDirt();
		o.kp_final[i].setDirt();
	}
	
	//std::cerr << "Num: " << s2->num() << std::endl;
	
	//tutto a posto, lancia il grafo!
	for (unsigned i = 1; i < N_T; ++i) {
		mutexes_main[i].unlock();
	}
	//MEASURE_ALL(all,
	PUSH_RANGE("SCALE 1 @ CPU", 0, MEMORY_CATEGORY)
	MEASURE(m, "CPU", "execution", "START", "start", 0,
	o.prepareFirstImage(im);
	);
	POP_RANGE
	
	PUSH_RANGE("OUT TRANSFER SCALE 1 @ CPU", 0, MEMORY_CATEGORY)
	MEASURE(m, "CPU", "execution", "START", "start", 0,
	o.images[0].finishProcessing();
	o.images[0].waitTransfer();
	);
	POP_RANGE
	
	//std::cout << "Num accessi previsti: " << o.images[0].s.num() << " | " << o.images[0].numElementOutAttached << std::endl << std::flush;
	//)
	// ESEGUI LA GPU
	elaborateQueue(&QUEUES[0], 1, m);
	
	// wait execution of ALL queues
	for (unsigned i = 1; i < N_T; ++i) {
		mutexes[i].lock();
	}
	
	//fprintf(stderr, "DONE COMPUTATION \n");
	POP_RANGE
	POP_RANGE
	MEASURE_ALL(all, m);
}
